Codondex

Sunday, January 6, 2019

Sex in humans can be traced to the evolution of a Sea Squirt and FuHc, a surreptitiously named gene. But, sexual pursuit didn’t initiate diversity of life, immunity from death did! In a previous article I explained how allorecognition in Sea Squirts, from different colonies resembled polarization at the contact point of an immune synapse between a Natural Killer cell (NK) and its target.

In early stage reproduction an “immunological paradox” occurs at the implantation site in the uterine wall where unexpectedly the (male fertilized) blastocyst is not rejected. Allorecognition occurs because maternal NK invades and remodels maternal blood vessels before the arrival of trophoblasts, the external cells of the blastocyst that carry male antigens, during formation of the placenta that provides the fetus with nutrients.

Sperm and ovum require gametes which are produced during meiotic cell division through phases of cellular polarization. Telophase (see image) resembles NK bound to its target cell just before the irreversible step that leads to perforin release and the target’s death. In this first Telophase, the yet to be separated pre-gametes contain twice the number of chromosomes, similarly when NK is enjoined to its target. Curiously, the inhibitory and activating NK receptors that bind target cells diversify only through meiosis where in gametes the centrosome required for polarization has degenerated.

During meiosis as every gamete was made, research showed the first enzymatic step in meiotic recombination provoked a transient burst of p53 activity. Now it seems equally possible these p53 bursts could be attributed to relaxed transposon (mobile DNA) controls that accompany the meiotic process. Indeed, meiotic double-strand breaks are the proximal trigger for retrotransposon eruptions including in animals lacking p53.

Mature NK can kill its target cell when combinations of inhibiting and activating receptors mediate a ‘non-self’ condition that triggers its polarization. At that point cytotoxic perforin and granzyme release toward the contact point, but NK can cancel the process. Reduced perforin release and binding in the immune synapse between these bound cells underlies the age-related decline in NK cytotoxicity. NK polarization and more generally cells undergoing division may also contribute to our understanding of ageing.

Here, we propose that certain features of the ageing process such as: (i) the increased reactivation rates of latent Mycobacterium tuberculosis, (ii) the slower resolution of inflammatory responses and (iii) the increased incidence of bacterial and fungal infection are attributable in part to an age-associated decline in NK function.

This paper demonstrated a pronounced age‐related impairment in perforin mobilization by NK to the K562 (target cell) contact point. It suggested that defective polarization to the immune synapse underlies the reduction in perforin secretion observed with age. The activation of mitogen‐activated protein kinases (MAPK) is critical for lytic granule (perforin-granzyme) polarization, granule exocytosis and NK Cytotoxicity. It is possible that these proximal signalling events are compromised by aging. In addition, it found that the studied p53 mutants regulated MAP2K3 gene whereas ectopic expression rescued the proliferative defect induced by mutant p53 knockdown.

Experimentally, NK that are absent of perforin or granzymes remain engaged five times longer. These locked-up NK cells would normally move to their next cell target to clear senescent or diseased cells, but cannot. NK suffering age-related reduced perforin release remain engaged awaiting a death signal from the target. This delay could be the single physiological cause of old or diseased cell accumulation in the body that is a primary cause of ageing. Perhaps polarization is causal?

Interestingly a member of the cytoskeleton superfamily Talin1 has been uniquely tied to two essential NK functions; 1. activation of LFA1, required for binding ICAM on NK target cell and, 2. NK polarization that results. We know overexpression of talin head activates LFA-1 and talin1 promotes cell proliferation by affecting the expression of BCL-2 family and p53 network. But, mdm2 the conserved nemesis of p53 is neutralized by Merlin, another cytoskeleton superfamily protein required for polarization. p53 also regulates the highly conserved Cdc42 which effects adhesion, actin cytoskeletal dynamics and cell movement including for angiogenesis in developing tumor microenvironments.

Furthermore, invalidation experiments demonstrate that PIF’s effects on placental apoptosis were mediated by TP53. Interestingly, the non-cancerous tissue associated with the over-expression of NK inhibiting ligand PCNA is the deciduas, and the only known NK cell subset that constitutively expresses NKp44 receptor for PCNA ligand is decidual NK cells. The PCNA gene is induced by p53 in the process of deciding cell fate, if PCNA is present in abundance in the absence of p53, DNA replication occurs.

However, fetal platelet antigens that cause miscarriages have been shown to prolong uterine/decidual NK survival, elevate NKp46 (LY94) receptor expression, perforin release and trophoblast apoptosis. The same receptor was also found to cluster at the NK-target cell immune synapse to regulate cytoskeletal organization and NK polarization on which perforin release is dependent.

Several common villous cells are implicated including NK, trophoblasts, vascular tip cells and cancer cells that may also induce NK to chaperone their effecting or replacing endo-epithelial cells lining host blood vessels. In the presence of senescent cells including NK with reduced perforin release, cancer cells may have discovered a trophoblast related back door to remodel blood vessels to serve transformed cells that ultimately grow to become cancerous tumors.

It’s distinctly possible that this p53 tug-o-war may be central to cellular age, death, reproduction and growth influenced by polarization and the perforin related effectiveness with which NK kills its meiosis evolved non-self targets.

At Precision Autology, the parent company of Codondex, we discover a patients p53 cell signatures to select their specific cancer cells, educate their natural killer cells to identify and kill the cancer that induced NK into its cooperative state.

Wednesday, December 26, 2018

Article authored by Kevin Bermeister
We argue the case that wide-ranging effects of the TP53 gene can be attributed to immunity, reproduction and cell death. Research has been selected to illustrate the probability that highly conserved elements of this protein have been central to the evolution of converged pathways and cellular systems in humans. Similarly, that innate and adaptive immunity share pathways that are not yet determined.

The relationship between p53 protein in a Natural Killer (NK) and its potentially diseased target arise from an elaborate, ancient, self preservation mechanism that can trace common lineage to replication in the earliest species of jawless invertebrate colonies. Individual colonies reproduce asexually, but individuals from two colonies first allorecognize their compatibility via their contacted ampullae and two individual immune reactions that determines the level of fusion or rejection. Every gene has two alleles, when two colonies share one or both FuHC alleles, they will fuse, whereas rejection occurs if no alleles are in common.

Jawless invertebrates like the “Sea Squirt” branched to jawed vertebrates, ultimately humans. In the the periodontal ligament of human jaws, between tooth and the jaw-bone Gingival fibroblasts co-mingle with cells of the immune system including innate NK cells.

In mammals, NK cells are large, granular lymphocytes that recognize MHC class I molecules on target cells via inhibitory NK cell receptors. After binding a target ligand the NK receptors can transmit an inhibitory signal that cancels a program for cytotoxic action previously triggered by contact with the target cell.

CD94 in vertebrates is one of the markers for NK cells and binds to MHC class I molecules. NK cell lineage has been traced through the CD94 family of receptors to Urochordates (like the Sea Squirt) where transplant immunity was described after tests conducted for immune resistance between colonies. It was shown that the gene, BsCD94-1, is differentially regulated during allorecognition and that a subpopulation of blood cells carries the corresponding receptor on its cell surface. Analysis of DNA from individual colonies and intronless BsCD94-1 probe revealed variation between individuals at the genomic level.

In Zebrafish, during reproduction p53 is central to the reversal of sex through apoptosis that kills one line of germ cells to yield either ovary or testes. The p53 pathway members have been investigated extensively in jawed vertebrates, and found that genes for all three Tp53 family members as well as Mdm2 and Mdm4 were present in the common ancestors. Homologs of all members of the p53 family as well as the negative regulators Mdm2 and Mdm4 are present in the Japanese lamprey (image right). Functional studies showed that sequence conservation was reflected in biochemical activity and the ability of lamprey-Mdm2 protein to function as a very effective E3 ubiquitin ligase (that degrades p53) in mouse cells.

Of the dozens of phospho-acceptor sites reported on p53 only three (Ser15, Thr18, Ser20) are highly conserved between humans and urochordates, the latter being where a bona-fide p53-MDM2 axis has appeared in evolution. Especially striking is the conservation of primary amino acid homology in the p53 transactivation domain between the invertebrate Sea Squirt and humans, indicating that as yet undefined evolutionary selection pressures have maintained this amino acid sequence at least since this Urochordate lineage.

Data strongly suggests that the p53 pathway plays an important role in human fertility. Identifying higher risk polymorphisms for pregnancy failure could provide patients with more accurate predictions of their IVF success rates. In humans, functional SNPs have been identified in both p53 and its negative regulator, Mdm2, which can alter the levels or function of p53. Interestingly, it appears that these SNPs (the p53 R72 allele and the G allele in Mdm2) are under evolutionary positive selection pressure in Caucasian and Asian populations.

The p53-mdm2 axis is implicated in Natural Killer cell neoplasms, which occur in mucosal sites. This is a rare but aggressive form of cancer of NK cells. These cells express high levels of mdm2 (an E3 ubiquitin ligase) that is highly effective degrading p53 and is presumed to be primarily responsible for a reduction in p53 levels. Alternatively PIDD-induced Caspase-2 directly cleaves Mdm2 at Asp 367, leading to loss of the C-terminal RING domain responsible for p53 ubiquitination. As a consequence, N-terminally truncated Mdm2 binds p53 and promotes its stability. Upon DNA damage, p53 induction of the Caspase-2-PIDDosome creates a positive feedback loop that inhibits Mdm2 and reinforces p53 stability and activity, contributing to cell survival and drug resistance.

In addition to regulating NK cell ligand expression, genetic reactivation of p53 in tumors can also induce a wide array of pro-inflammatory mediators ranging from adhesion receptor (ICAM-1) expression [ligand for LFA-1] to the production of various chemokines (CXCL11 and monocyte chemoattractant protein-1) and cytokines (interleukin-15). Furthermore, recent studies in anti-viral immunity indicate that several interferon-inducible genes and Toll-like receptor-3 expression are direct transcriptional targets of p53 and that p53 contributes to production of type I interferon by virally infected cells.

Gingival fibroblasts, present in the alveolar bone-lining mucosa (i.e., the gingiva), are thought to play a role in the recruitment of immune cells toward the inflamed periodontium. There is extensive literature suggesting that monocytes can differentiate into pre-osteoclasts and eventually fuse into multinucleated bone-resorbing osteoclasts. The role of GF's in osteoclastogenesis was shown by in vitro cocultures of GFs with peripheral blood mononuclear cells (PBMCs), where osteoclast-like cells formed after 21 days. Cell–cell contact between gingival or periodontal ligament (PDL) fibroblasts from the periodontium and osteoclast precursors is required for osteoclastogenesis. We hypothesized that GF's play a role in the retention, survival, and proliferation of lymphocytes. Our findings show that GF's support the retention and survival of T, B, and NK cells by LFA-1 expression [that binds to ICAM-1]. Importantly, expression did not differ over time, which indicates that it is a process, which is initiated before 14 days and stays constant after 21 days, explaining the survival and retention of lymphocytes in GF cocultures.

Incidentally it has been shown that phagocytosis in coral reef immunity is linked to Cdc42, a gene responsible for cell cycle regulation and division. Data also demonstrates a previously uncharacterized role for p53 in regulating Cdc42-dependent cell effects that control actin cytoskeletal dynamics and cell movement.

Image (A,B,C) illustrates the immune reaction at touching tips of ampullae of jawless invertebrates. It resembles immune response between NK and its target including the formation of immune synapse and granularization. The synapse, allorecognition and transfer of cells also echoes the origin of sexual reproduction between fusing individuals.

An explanation is applicable to the tumor suppressor gene p53, which has conflicting roles on the induction of autophagy (and subsequent cell death) depending on its location in the nucleus or cytoplasm. Similar to toxin-antitoxin systems in bacteria, some have questioned whether p53 would be better characterized as a cell-death or a cell-survival gene. After initial contact it is proposed that a NK cell would determine its targets’ ultimate fate through receptor mediated exosomes containing miRNA samples that induce p53 isoforms to confirm non-self. The p53 pathway regulates the production of exosomes into the medium and these vesicles can communicate with adjacent cells and even cells of the immune system. Such an important mechanism could have productive or devastating effects.

Individual cases support the hypothesis that p53 in NK and target cells are coordinated in this process. One in particular involved a mirrored mutation. A point mutation in exon 7 of the p53 gene was detected in the KHYG-1 cells and direct sequencing revealed the conversion of C to T at nucleotide 877 in codon 248. The primary leukemia cells also carried the same point mutation. Although the precise role of the p53 point mutation in leukemogenesis remains to be clarified, the establishment of an NK leukemia cell line with a p53 point mutation could be valuable.

The features of a lymphoblastoid natural killer (NK)-cell lymphoma presenting in the skin in a young caucasian woman are described. The disease behaved aggressively, but long-lasting remission was obtained by combination chemotherapy followed by autologous bone marrow transplantation. The blastoid cells were positive for terminal deoxynucleotidyl transferase, CD34, CD56 and CD4. Furthermore, the NK-cell receptor complex CD94/NKG2 was strongly expressed. Molecular analysis showed no abnormalities of the CDKN2A (p16), CDKN2B (p15) or TNFRSF6 (Fas) genes. By contrast, a 34-bp deletion in exon 7 of the TP53 (p53) gene was detected. It is suggested that lymphoblastoid NK-cell lymphoma, which is a rare but distinctive disease, originates from NK cell precursors and may be associated with and possibly caused by alterations in the TP53 gene.

In one patient the same intronic point mutation was found in the tumor cell line derived from a bone marrow metastasis and in multiple liver metastases but not in normal DNA, indicating that it occurred as a somatic event before the development of these metastases. These findings further support the role of inactivation of the p53 gene in the pathogenesis of lung cancer and indicate the role of intronic point mutation in this process.

Our objective at Precision Autology is to compute Codondex iScore's on introns and p53 mRNA to identify highly a patient's desirable cells. After coculturing these cells with a patient's NK cells, sufficient maturity for immune response is expected to be achieved. On reintroduction to the patient, NK rejection of unrecognized tumor cells is expected to stimulate a Dendrite led immune cascade. The precise sequence of events follows from initial cell selection and immune education that cascades to a full and appropriate immune response.

Monday, November 26, 2018

We built a model to determine whether a random or non-random relationship existed between introns and proteins of a transcript. We determined the relationship was overwhelmingly non-random and progressed to study particular genes in more detail.

Based on our studies we suggested TP53 readily encodes specific isoform concentrations that alter next generation transcriptions and introns play significant roles. Here we validate our selection logic and describe its proposed use in immunotherapy. From intron1, we computed +400k k-mers from which we selected 8 short k-mers out of 12 TP53 and 29 BRCA1 transcripts. We synthesized the sequences and In subsequent transfection experiments 3/3 TP53 and 3/5 BRCA1 significantly (p<0.05) reduced the rate of proliferating HeLa cells.

Selection Background

For each transcript we first computed intron1 k-mers greater than 7 oligos (see image below, each k-mer has an Offset#). For all k-mer’s we computed a signature and associated it with a signature of the transcripts’ protein. In Offset# order, for each k-mer of each transcript we ordered transcripts according to the result of a k-mer:protein signature ordering. For each offset# (k-mer) we recorded the order of each transcript in a vector.

In offset# (computation) order, we observed the next vector to discover any changes in ordering of transcripts. After filtering k-mers for a length change, more than 90% of transcript ordering remained stable. Occasionally one or two transcripts changed position, very rarely more than 75% of transcripts in the vector changed position. So when we discovered a few vectors with >75% change we extracted them and subjected them to a selection algorithm that identified 8 short, 28 oligo sequences from the 41 transcripts processed.

Codondex iScoreTM ordering, comparison and selection algorithms consider that transcripts compared at sequential k-mers represents a compelling method to identify sequences that “stand out from the crowd” because they may be inherent upstream of transcription. Potential of any k-mer exists to aggregate or contribute to the formation of coacervates in a sequence and length dependent manner. In the image above red text represents our computation of the first 14 of 135 potential k-mer’s of the identical 23 oligo sequence. For each k-mer all k-mers of the 23 oligo sequence would be queried (in both directions) and repetitions counted. For example of the 14 k-mers, the k-mer at Offset#0 can also be found in Offsets#2,5,9 and 14.

In the compound computation of the 23 letter sequence, Offset#0 GTGGGAAT is repeated in 16 other k-mers and Offset#135 GTGGGAATCTTATCCATGACCCA has 136 k-mers repeated in it (including itself). When looking at the entire intron sequence (or any long sequence) there is never a linear progression of k-mers, inevitably the counts becomes disordered.

In the following example of ordering transcript computations for a single Offset#, each result has been ordered in a vector of 15 men1 transcripts. Each protein signature is constant for every Offset# because the signature is computed from the entire string. Some protein signatures are identical, but not intron signatures. Transcripts with identical protein signatures are preferentially sorted to give final order to transcripts in the vector.

In our detailed review of each transcript we discovered that the compound effect of k-mer repeats described an inherent structure of relationships between nucleotides lengths. We considered how varied transcription events would alter the representations of these non-coding oligo lengths in their ncRNA form. For example, Offset#135 included 136 repeats of k-mers, which statistically infers it has a greater chance of survival and/or function in any of its constitutive parts than a k-mer with a lesser number of repeats.

As stated in the opening paragraph we synthesized 8 short RNA selections we made using our vectors to discover how they translated in biology. In future we intend to compute p53 (or other gene) transcripts from multiple samples of a patient biopsy. We do this by separating cells into multiple wells, running RNAseq on each well and computing the transcript position of each well in our p53 vectors. Once we identify the logarithmic proximity of each wells transcript to other transcripts we will select a well. We will use selected cells to educate natural killer cells extracted from the patient and return the immune cells only to the patient to reduce proliferation of diseased cells. We hope to bring this therapy to the market in the next few years.

In subsequence experiments we used our bioinformatic to identify a specific profile of HeLa cell sequences that reduced proliferation. To discover these sequences, we computed more than 400,000 intron1 k-mers from 41 transcripts, identified and transfected 6 short sequences that significantly reduced the growth of these cervical cancer cells. From our RNA-seq analysis growth was reduced by shifting replicative senescence to apoptosis accompanied by mitcochondrial hyper-function.

For transcript (T), we looked at any sequence (S) as having cell-wide potential to define or be re-defined during or post transcription. However, as DNA of a gene, S must be considered to possess a unique potential that will effect its interactions. Science knows very little about the catalog of possible effects that can be attributed to S. To model its potential qualities we first look to its structural arrangements for any length (n>7).

In our examples below, for S1=TGTGGGCCCACA and S2=GTGGGCCCAGAC we computed every possible k(n>7) and count unions of k - ⋃k.

The Unions of k for S1 and S2

Why is this important? Let's say you want to tie this computation to biological function, the base data would have to represent the myriad underlying biological possibilities. To do this you would need very low level analytical resolution, some of which is represented in the images above.

At Codondex we take a further step by associating every k with a hash signature (#s) of the transcript protein or mRNA. You will appreciate for T that #s is constant and associated with every k(n>7) input to tuple k(n>7...S):#s. The combination of each intron 1 k-mer with its transcript protein signature becomes the input to a highly ordered vector used to compare order stability, in the vector at the next nucleotide of k-mer:protein signatures for multiple transcripts of the same gene.

Our results show at the next nucleotide that order changes, in the vector are rare and orient dramatically to k-mer's of shorter lengths. Further, that the transcripts with the most changes in vector positions are definitive for sequence selections that confer their anti-proliferation effect in transfection experiments.

We anticipate cells approximating these k-mer:protein anti-proliferation signatures will be most useful in the fight against proliferating diseased cells. To test usefulness we will co-culture these cells to precondition Natural Killer cells with conforming and non-conforming receptor-ligand relationship sensitivity. These Natural Killer cells will be tested against HeLa to determine whether recognition and optimal immune response can be triggered.

Stay posted for more updates on our exciting discoveries and computations for minimally manipulated autologous therapy against patient disease.

Monday, August 13, 2018

Sometime back we published a paper that expanded on TP53 intron1 k-mer relationships we have been investigating using our algorithms. We described how TP53 response elements bind p53 monomers and more complex response elements bind and form P53 tetramer's that support known transcription events.

Since that time we have been conducting numerous laboratory tests in conjunction with Professor Noam Shomron at TelAviv University to confirm that sequences we identified from our TP53 bioinformatic produced predictable results that were precisely directed in cells.

From our initial results, it appears we can elicit an important relationship between intron1 and sequenced proteins of same transcripts. Further that these relationships are non-random and that they can be used to identify the highly specific DNA intron1 sequences that drive this non-randomness.

We previously published the chart below indicating that men1 k-mers ordered into a 15-transcript vector were producing length bias despite our algorithm being length agnostic. The scatter-graph is a plot of k-mers (15 transcripts) by intra transcript-repeats:length (horizontal axis) that form transcript color bands and express each transcripts length agnostic k-mer repeat-profile.

On the basis that repeats for (length)ATCG(count) would be expressed as (4)ATCG(3), (3)ATC(1), (3)TCG(1), (2)TC(3) and (2)CG(2), the count for (4)ATCG(3) equates with (2)TC(3).

15 men1 Intron1 Transcripts - kmer repeats

Relying on the unique ordering for each transcripts k-mer in the vector, we made selections of TP53 k-mers where transcript order in vectors most significantly changed compared to the previous vector. For this we discovered that most disrupted vectors were caused by k-mers of very low lengths. Further, in comparison almost all transcript positions in most vectors remained stable.

12 TP53 Intron1 Transcripts

Ordering in our vectors is a way to represent transcript k-mers where computation is sequential from the first oligo of intron1. Each k-mer contributes its vector ordering based on its relationship to the transcripts' constant, protein or mRNA signature.

Sunday, January 15, 2017

Concentration dynamics influence DNA-Protein-RNA-Protein interactions in both the nucleus and cytoplasm are a significant influencer of activity. It is becoming increasingly evident that extremely small changes, often in a small fraction of concentrations that results from dosage sensitive genes can be immediately significant.

Subcellular concentrations are affected by elements including DNA, RNA, protein, metabolites, molecules and water. This concentrated environment can determine binding of particular elements in the pressure of condensed space. The effects on intrinsic protein disorder, including p53 is also concentration dependent and dosage sensitive. Double stranded DNA association and dissociation, as influenced by epigenetic factors and induced by proteins may play a role in organizing DNA’s subsequence concentrations to be more or less favorable to binding protein, transcription efficiency and translation.

Intra chromosome, any sub-sequences of the DNA molecule can be thought of as concentrations. Each gene is then a particular pattern of inherent purine-pyrimidine concentrations, proximal definitions on DNA of the chromosome. Therefore, our interest in DNA concentration specifics led us to review every adjacent combination of any of the four letters A,G,C or T within a gene of interest. Our DNA pattern algorithmcomputes every possible subsequence >7 letters of a DNA sequence. It is sensitive to the smallest single nucleotide change in any subsequence >7 for all possible sub-sequences of a transcript.

From the transcribed DNA sequence GAGCTTCGAG6 there can momentarily exist, as RNA derivative sub-sequences or sub-concentrations >7 GAGCTTCG3| AGCTTCGA4| GAGCTTCGA4| GCTTCGAG3| AGCTTCGAG4|:- The subscript is the maximum frequency the subsequence can be discovered in any >7 derivations of GAGCTTCGAG. Since order of letters in DNA is fixed, our model represents the complete, potential set of weighted RNA segments that can be transcribed into the nucleus and cytoplasm.

Because various concentrations of repeatedly transcribed DNA sequences will exist as RNA in the cell at various times, this is an exhaustive model against which a single gene-cell potential state can be computed. Measuring and sequencing gene-cell cytoplasmic RNA concentrations and comparing them to the gene-cell potential state provides a comparative. Using this as a diagnostic may be a sensitive measure to associate non-coding DNA-RNA states with diseases.

Tuesday, December 6, 2016

We analyzed data from 12 - P53 transcripts by following 2765 theoretical k-mers selected because they started with the same two letters in every transcript. We sorted these by their Sequence Start (in the origin DNA), iScore (col C) then by k-mer recurrence in the transcripts' bigger length k-mers (col J) [column reference is to the linked file].In the chart below, transcript k-mers that demonstrated sequential PiV stability produced uninterrupted lines. k-mer stability/instability is a function of iScore (discussed above) and plot of the sequence of k-mer PiV [value's].We plotted each k-mer Position in iScore vector (PiV) for each transcript. For each transcript, each k-mer can occupy one of twelve positions and each PiV for all transcripts must be occupied 2765 times (see last chart below)

At row 2459 above (top left), there is a single line perturbation (not visible in this image). We traced it to k-mer (#939134) where we discovered that the addition of "G" at nucleotide 1377 caused Transcript T793 to change order with T597, T465. The result of "G" was an iSocre change that caused the order of transcripts to flip.

The next image demonstrate the total PiV (row1) distributions for all 2765 k-mers of each transcript (col N).